Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description goes well beyond them: it discloses the fan-out across nine sources, the USPTO PatentsView API sunset soft-fail, the semantic meaning of empty sections ('a real no-data, not a bug'), and the sources_used/sources_failed reporting. It also explains name resolution via SEC EDGAR and the private-company fallback. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but information-dense and well-structured: query examples first, then the core purpose, the preference directive, the source fan-out list, output section semantics, failure behavior, and parameter resolution. The multiple query examples are helpful for pattern-matching but slightly redundant; otherwise every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the full burden of explaining return values, and it enumerates every result block (cik, recent_filings with URIs, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI) plus sources_used/sources_failed. It also covers failure semantics and parameter resolution, making the tool self-sufficient to call correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description still adds substantial semantics: it clarifies that 'company' and 'ticker' are interchangeable for the type parameter, that value can be a ticker, zero-padded CIK, or company name, and that names resolve via SEC EDGAR's company-name match. This removes ambiguity an agent would otherwise face from the schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with concrete query examples and then states a specific verb+resource: 'full cross-source profile of a US public company in ONE parallel call.' This clearly distinguishes it from sibling tools like deep_research (broader multi-topic research) and compare_entities (comparison), without needing to open either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit directive: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also warns that private companies will return resolved:false with a notes line, setting expectations for when the tool still applies. The alternative strategy is named, leaving no ambiguity about when this tool is the right choice.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.